
Configure AI Models Instead of Hard-Coding Them
Keep model names in configuration, select models by task, and compare routes using the cost of completed work.

Keep model names in configuration, select models by task, and compare routes using the cost of completed work.

Why pull-request-controlled AI review instructions collapse a trust boundary, and how teams can restore it without giving up useful repository context.

Kimi K3's released near-frontier weights reduce dependence on US model providers and make price, deployment control and licence terms more important.

Delegate clearly, ask for evidence, and build trust in AI output one task at a time.

US review of frontier model releases can reduce specific security risks, but delays need public criteria, time limits and proportionate controls.

A speculative look at AI-generated mini apps, VibeOS, Google Search, and why durable software survives where state, trust, and depth matter.

How I use Codex, JetBrains IDEs, Copilot review, AGENTS.md, reusable skills, and CI checks to make AI coding reliable.

What current UK evidence says about AI exposure, adoption, entry-level work, and the roles most likely to change.